Overview

Dataset statistics

Number of variables25
Number of observations53
Missing cells30
Missing cells (%)2.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.5 KiB
Average record size in memory202.4 B

Variable types

Categorical6
Numeric19

Alerts

CAR has a high cardinality: 53 distinct valuesHigh cardinality
Model has a high cardinality: 53 distinct valuesHigh cardinality
Price is highly overall correlated with Power and 19 other fieldsHigh correlation
Power is highly overall correlated with Price and 19 other fieldsHigh correlation
Torque is highly overall correlated with Price and 18 other fieldsHigh correlation
Battery is highly overall correlated with Price and 20 other fieldsHigh correlation
Range is highly overall correlated with Price and 15 other fieldsHigh correlation
Wheelbase is highly overall correlated with Price and 20 other fieldsHigh correlation
Length is highly overall correlated with Price and 21 other fieldsHigh correlation
Width is highly overall correlated with Price and 20 other fieldsHigh correlation
Height is highly overall correlated with Seats and 3 other fieldsHigh correlation
Weight is highly overall correlated with Price and 20 other fieldsHigh correlation
Permissible_weight is highly overall correlated with Price and 20 other fieldsHigh correlation
Capacity is highly overall correlated with Price and 16 other fieldsHigh correlation
Seats is highly overall correlated with Height and 3 other fieldsHigh correlation
Tire_size is highly overall correlated with Price and 17 other fieldsHigh correlation
Max_speed is highly overall correlated with Price and 17 other fieldsHigh correlation
Boot_capacity is highly overall correlated with Price and 18 other fieldsHigh correlation
Acceleration is highly overall correlated with Price and 18 other fieldsHigh correlation
DC is highly overall correlated with Price and 21 other fieldsHigh correlation
Energy_consumption is highly overall correlated with Price and 15 other fieldsHigh correlation
CAR is highly overall correlated with Price and 23 other fieldsHigh correlation
Make is highly overall correlated with Battery and 9 other fieldsHigh correlation
Model is highly overall correlated with Price and 23 other fieldsHigh correlation
Brakes is highly overall correlated with Battery and 10 other fieldsHigh correlation
Drive is highly overall correlated with Price and 18 other fieldsHigh correlation
Doors is highly overall correlated with Price and 10 other fieldsHigh correlation
Doors is highly imbalanced (61.3%)Imbalance
Brakes has 1 (1.9%) missing valuesMissing
Permissible_weight has 8 (15.1%) missing valuesMissing
Capacity has 8 (15.1%) missing valuesMissing
Boot_capacity has 1 (1.9%) missing valuesMissing
Acceleration has 3 (5.7%) missing valuesMissing
Energy_consumption has 9 (17.0%) missing valuesMissing
CAR is uniformly distributedUniform
Model is uniformly distributedUniform
CAR has unique valuesUnique
Model has unique valuesUnique

Reproduction

Analysis started2023-10-25 00:59:04.139033
Analysis finished2023-10-25 00:59:37.819386
Duration33.68 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

CAR
Categorical

HIGH CARDINALITY  HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct53
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size552.0 B
Audi e-tron 55 quattro
 
1
Opel Mokka-e
 
1
Peugeot e-2008
 
1
Porsche Taycan 4S (Performance)
 
1
Porsche Taycan 4S (Performance Plus)
 
1
Other values (48)
48 

Length

Max length36
Median length24
Mean length19.90566
Min length6

Characters and Unicode

Total characters1055
Distinct characters64
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique53 ?
Unique (%)100.0%

Sample

1st rowAudi e-tron 55 quattro
2nd rowAudi e-tron 50 quattro
3rd rowAudi e-tron S quattro
4th rowAudi e-tron Sportback 50 quattro
5th rowAudi e-tron Sportback 55 quattro

Common Values

ValueCountFrequency (%)
Audi e-tron 55 quattro 1
 
1.9%
Opel Mokka-e 1
 
1.9%
Peugeot e-2008 1
 
1.9%
Porsche Taycan 4S (Performance) 1
 
1.9%
Porsche Taycan 4S (Performance Plus) 1
 
1.9%
Porsche Taycan Turbo 1
 
1.9%
Porsche Taycan Turbo S 1
 
1.9%
Renault Zoe R110 1
 
1.9%
Renault Zoe R135 1
 
1.9%
Skoda Citigo-e iV 1
 
1.9%
Other values (43) 43
81.1%

Length

2023-10-24T21:59:37.883401image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
model 7
 
4.0%
tesla 7
 
4.0%
e-tron 6
 
3.4%
s 6
 
3.4%
audi 6
 
3.4%
performance 6
 
3.4%
quattro 6
 
3.4%
kia 4
 
2.3%
porsche 4
 
2.3%
taycan 4
 
2.3%
Other values (71) 121
68.4%

Most occurring characters

ValueCountFrequency (%)
124
 
11.8%
e 93
 
8.8%
a 73
 
6.9%
o 71
 
6.7%
r 56
 
5.3%
n 49
 
4.6%
t 38
 
3.6%
l 33
 
3.1%
i 33
 
3.1%
s 33
 
3.1%
Other values (54) 452
42.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 664
62.9%
Uppercase Letter 168
 
15.9%
Space Separator 124
 
11.8%
Decimal Number 55
 
5.2%
Dash Punctuation 24
 
2.3%
Other Punctuation 7
 
0.7%
Initial Punctuation 4
 
0.4%
Open Punctuation 4
 
0.4%
Close Punctuation 4
 
0.4%
Math Symbol 1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 93
14.0%
a 73
11.0%
o 71
10.7%
r 56
 
8.4%
n 49
 
7.4%
t 38
 
5.7%
l 33
 
5.0%
i 33
 
5.0%
s 33
 
5.0%
u 31
 
4.7%
Other values (14) 154
23.2%
Uppercase Letter
ValueCountFrequency (%)
S 21
 
12.5%
P 19
 
11.3%
M 17
 
10.1%
T 13
 
7.7%
W 9
 
5.4%
R 8
 
4.8%
C 8
 
4.8%
V 7
 
4.2%
A 7
 
4.2%
K 6
 
3.6%
Other values (13) 53
31.5%
Decimal Number
ValueCountFrequency (%)
3 14
25.5%
0 9
16.4%
4 7
12.7%
5 7
12.7%
2 6
10.9%
1 4
 
7.3%
9 3
 
5.5%
6 3
 
5.5%
8 2
 
3.6%
Other Punctuation
ValueCountFrequency (%)
. 6
85.7%
! 1
 
14.3%
Space Separator
ValueCountFrequency (%)
124
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 24
100.0%
Initial Punctuation
ValueCountFrequency (%)
« 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 832
78.9%
Common 223
 
21.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 93
 
11.2%
a 73
 
8.8%
o 71
 
8.5%
r 56
 
6.7%
n 49
 
5.9%
t 38
 
4.6%
l 33
 
4.0%
i 33
 
4.0%
s 33
 
4.0%
u 31
 
3.7%
Other values (37) 322
38.7%
Common
ValueCountFrequency (%)
124
55.6%
- 24
 
10.8%
3 14
 
6.3%
0 9
 
4.0%
4 7
 
3.1%
5 7
 
3.1%
. 6
 
2.7%
2 6
 
2.7%
« 4
 
1.8%
( 4
 
1.8%
Other values (7) 18
 
8.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1047
99.2%
None 8
 
0.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
124
 
11.8%
e 93
 
8.9%
a 73
 
7.0%
o 71
 
6.8%
r 56
 
5.3%
n 49
 
4.7%
t 38
 
3.6%
l 33
 
3.2%
i 33
 
3.2%
s 33
 
3.2%
Other values (52) 444
42.4%
None
ValueCountFrequency (%)
à 4
50.0%
« 4
50.0%

Make
Categorical

Distinct20
Distinct (%)37.7%
Missing0
Missing (%)0.0%
Memory size552.0 B
Tesla
Audi
Kia
Porsche
Volkswagen
Other values (15)
28 

Length

Max length13
Median length8
Mean length5.7924528
Min length2

Characters and Unicode

Total characters307
Distinct characters39
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)9.4%

Sample

1st rowAudi
2nd rowAudi
3rd rowAudi
4th rowAudi
5th rowAudi

Common Values

ValueCountFrequency (%)
Tesla 7
13.2%
Audi 6
 
11.3%
Kia 4
 
7.5%
Porsche 4
 
7.5%
Volkswagen 4
 
7.5%
Hyundai 3
 
5.7%
BMW 3
 
5.7%
Nissan 3
 
5.7%
Honda 2
 
3.8%
Mercedes-Benz 2
 
3.8%
Other values (10) 15
28.3%

Length

2023-10-24T21:59:37.982965image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tesla 7
13.2%
audi 6
 
11.3%
kia 4
 
7.5%
porsche 4
 
7.5%
volkswagen 4
 
7.5%
hyundai 3
 
5.7%
bmw 3
 
5.7%
nissan 3
 
5.7%
renault 2
 
3.8%
citroã«n 2
 
3.8%
Other values (10) 15
28.3%

Most occurring characters

ValueCountFrequency (%)
a 32
 
10.4%
e 31
 
10.1%
s 23
 
7.5%
i 20
 
6.5%
n 19
 
6.2%
l 15
 
4.9%
d 15
 
4.9%
o 15
 
4.9%
u 14
 
4.6%
r 11
 
3.6%
Other values (29) 112
36.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 239
77.9%
Uppercase Letter 64
 
20.8%
Initial Punctuation 2
 
0.7%
Dash Punctuation 2
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 32
13.4%
e 31
13.0%
s 23
9.6%
i 20
8.4%
n 19
7.9%
l 15
 
6.3%
d 15
 
6.3%
o 15
 
6.3%
u 14
 
5.9%
r 11
 
4.6%
Other values (10) 44
18.4%
Uppercase Letter
ValueCountFrequency (%)
M 7
10.9%
T 7
10.9%
A 6
9.4%
P 6
9.4%
B 5
 
7.8%
H 5
 
7.8%
V 4
 
6.2%
K 4
 
6.2%
S 4
 
6.2%
W 3
 
4.7%
Other values (7) 13
20.3%
Initial Punctuation
ValueCountFrequency (%)
« 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 303
98.7%
Common 4
 
1.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 32
 
10.6%
e 31
 
10.2%
s 23
 
7.6%
i 20
 
6.6%
n 19
 
6.3%
l 15
 
5.0%
d 15
 
5.0%
o 15
 
5.0%
u 14
 
4.6%
r 11
 
3.6%
Other values (27) 108
35.6%
Common
ValueCountFrequency (%)
« 2
50.0%
- 2
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 303
98.7%
None 4
 
1.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 32
 
10.6%
e 31
 
10.2%
s 23
 
7.6%
i 20
 
6.6%
n 19
 
6.3%
l 15
 
5.0%
d 15
 
5.0%
o 15
 
5.0%
u 14
 
4.6%
r 11
 
3.6%
Other values (27) 108
35.6%
None
ValueCountFrequency (%)
« 2
50.0%
à 2
50.0%

Model
Categorical

HIGH CARDINALITY  HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct53
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size552.0 B
e-tron 55 quattro
 
1
Mokka-e
 
1
e-2008
 
1
Taycan 4S (Performance)
 
1
Taycan 4S (Performance Plus)
 
1
Other values (48)
48 

Length

Max length28
Median length18
Mean length13.113208
Min length1

Characters and Unicode

Total characters695
Distinct characters59
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique53 ?
Unique (%)100.0%

Sample

1st rowe-tron 55 quattro
2nd rowe-tron 50 quattro
3rd rowe-tron S quattro
4th rowe-tron Sportback 50 quattro
5th rowe-tron Sportback 55 quattro

Common Values

ValueCountFrequency (%)
e-tron 55 quattro 1
 
1.9%
Mokka-e 1
 
1.9%
e-2008 1
 
1.9%
Taycan 4S (Performance) 1
 
1.9%
Taycan 4S (Performance Plus) 1
 
1.9%
Taycan Turbo 1
 
1.9%
Taycan Turbo S 1
 
1.9%
Zoe R110 1
 
1.9%
Zoe R135 1
 
1.9%
Citigo-e iV 1
 
1.9%
Other values (43) 43
81.1%

Length

2023-10-24T21:59:38.087967image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
model 7
 
5.6%
e-tron 6
 
4.8%
quattro 6
 
4.8%
performance 6
 
4.8%
s 6
 
4.8%
taycan 4
 
3.2%
plus 4
 
3.2%
long 4
 
3.2%
range 4
 
3.2%
sportback 3
 
2.4%
Other values (51) 74
59.7%

Most occurring characters

ValueCountFrequency (%)
71
 
10.2%
e 62
 
8.9%
o 56
 
8.1%
r 45
 
6.5%
a 41
 
5.9%
t 30
 
4.3%
n 30
 
4.3%
c 23
 
3.3%
- 22
 
3.2%
l 18
 
2.6%
Other values (49) 297
42.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 425
61.2%
Uppercase Letter 104
 
15.0%
Space Separator 71
 
10.2%
Decimal Number 55
 
7.9%
Dash Punctuation 22
 
3.2%
Other Punctuation 7
 
1.0%
Close Punctuation 4
 
0.6%
Open Punctuation 4
 
0.6%
Initial Punctuation 2
 
0.3%
Math Symbol 1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 62
14.6%
o 56
13.2%
r 45
10.6%
a 41
9.6%
t 30
 
7.1%
n 30
 
7.1%
c 23
 
5.4%
l 18
 
4.2%
u 17
 
4.0%
i 13
 
3.1%
Other values (13) 90
21.2%
Uppercase Letter
ValueCountFrequency (%)
S 17
16.3%
P 13
12.5%
M 10
 
9.6%
R 6
 
5.8%
W 6
 
5.8%
C 6
 
5.8%
T 6
 
5.8%
L 5
 
4.8%
I 5
 
4.8%
E 5
 
4.8%
Other values (9) 25
24.0%
Decimal Number
ValueCountFrequency (%)
3 14
25.5%
0 9
16.4%
4 7
12.7%
5 7
12.7%
2 6
10.9%
1 4
 
7.3%
9 3
 
5.5%
6 3
 
5.5%
8 2
 
3.6%
Other Punctuation
ValueCountFrequency (%)
. 6
85.7%
! 1
 
14.3%
Space Separator
ValueCountFrequency (%)
71
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 22
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Initial Punctuation
ValueCountFrequency (%)
« 2
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 529
76.1%
Common 166
 
23.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 62
 
11.7%
o 56
 
10.6%
r 45
 
8.5%
a 41
 
7.8%
t 30
 
5.7%
n 30
 
5.7%
c 23
 
4.3%
l 18
 
3.4%
u 17
 
3.2%
S 17
 
3.2%
Other values (32) 190
35.9%
Common
ValueCountFrequency (%)
71
42.8%
- 22
 
13.3%
3 14
 
8.4%
0 9
 
5.4%
4 7
 
4.2%
5 7
 
4.2%
2 6
 
3.6%
. 6
 
3.6%
1 4
 
2.4%
) 4
 
2.4%
Other values (7) 16
 
9.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 691
99.4%
None 4
 
0.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
71
 
10.3%
e 62
 
9.0%
o 56
 
8.1%
r 45
 
6.5%
a 41
 
5.9%
t 30
 
4.3%
n 30
 
4.3%
c 23
 
3.3%
- 22
 
3.2%
l 18
 
2.6%
Other values (47) 293
42.4%
None
ValueCountFrequency (%)
à 2
50.0%
« 2
50.0%

Price
Real number (ℝ)

Distinct50
Distinct (%)94.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean246158.51
Minimum82050
Maximum794000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size552.0 B
2023-10-24T21:59:38.208063image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum82050
5-th percentile98536
Q1142900
median178400
Q3339480
95-th percentile482565.8
Maximum794000
Range711950
Interquartile range (IQR)196580

Descriptive statistics

Standard deviation149187.49
Coefficient of variation (CV)0.60606268
Kurtosis2.7088429
Mean246158.51
Median Absolute Deviation (MAD)53400
Skewness1.567053
Sum13046401
Variance2.2256906 × 1010
MonotonicityNot monotonic
2023-10-24T21:59:38.309989image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
139900 3
 
5.7%
142900 2
 
3.8%
345700 1
 
1.9%
124900 1
 
1.9%
457000 1
 
1.9%
482283 1
 
1.9%
653000 1
 
1.9%
794000 1
 
1.9%
135900 1
 
1.9%
82050 1
 
1.9%
Other values (40) 40
75.5%
ValueCountFrequency (%)
82050 1
 
1.9%
96900 1
 
1.9%
97990 1
 
1.9%
98900 1
 
1.9%
122900 1
 
1.9%
124900 1
 
1.9%
125000 1
 
1.9%
128900 1
 
1.9%
135900 1
 
1.9%
139900 3
5.7%
ValueCountFrequency (%)
794000 1
1.9%
653000 1
1.9%
482990 1
1.9%
482283 1
1.9%
457000 1
1.9%
443990 1
1.9%
426200 1
1.9%
414900 1
1.9%
407990 1
1.9%
368990 1
1.9%

Power
Real number (ℝ)

Distinct27
Distinct (%)50.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean269.77358
Minimum82
Maximum772
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size552.0 B
2023-10-24T21:59:38.403144image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum82
5-th percentile83
Q1136
median204
Q3372
95-th percentile625
Maximum772
Range690
Interquartile range (IQR)236

Descriptive statistics

Standard deviation181.29859
Coefficient of variation (CV)0.67203981
Kurtosis0.63186188
Mean269.77358
Median Absolute Deviation (MAD)69
Skewness1.2013562
Sum14298
Variance32869.179
MonotonicityNot monotonic
2023-10-24T21:59:38.491716image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
136 12
22.6%
204 7
 
13.2%
360 2
 
3.8%
625 2
 
3.8%
503 2
 
3.8%
772 2
 
3.8%
184 2
 
3.8%
525 2
 
3.8%
82 2
 
3.8%
83 2
 
3.8%
Other values (17) 18
34.0%
ValueCountFrequency (%)
82 2
 
3.8%
83 2
 
3.8%
108 1
 
1.9%
109 1
 
1.9%
135 1
 
1.9%
136 12
22.6%
145 1
 
1.9%
150 1
 
1.9%
154 1
 
1.9%
170 1
 
1.9%
ValueCountFrequency (%)
772 2
3.8%
625 2
3.8%
525 2
3.8%
503 2
3.8%
490 1
1.9%
480 1
1.9%
435 1
1.9%
408 1
1.9%
400 1
1.9%
372 1
1.9%

Torque
Real number (ℝ)

Distinct31
Distinct (%)58.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean460.03774
Minimum160
Maximum1140
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size552.0 B
2023-10-24T21:59:38.601295image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum160
5-th percentile211.2
Q1260
median362
Q3640
95-th percentile1003.8
Maximum1140
Range980
Interquartile range (IQR)380

Descriptive statistics

Standard deviation261.647
Coefficient of variation (CV)0.56875117
Kurtosis0.48215372
Mean460.03774
Median Absolute Deviation (MAD)102
Skewness1.1836086
Sum24382
Variance68459.152
MonotonicityNot monotonic
2023-10-24T21:59:38.685131image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
260 7
 
13.2%
395 6
 
11.3%
270 3
 
5.7%
310 3
 
5.7%
1140 2
 
3.8%
160 2
 
3.8%
540 2
 
3.8%
755 2
 
3.8%
664 2
 
3.8%
315 2
 
3.8%
Other values (21) 22
41.5%
ValueCountFrequency (%)
160 2
 
3.8%
210 1
 
1.9%
212 1
 
1.9%
225 1
 
1.9%
245 1
 
1.9%
250 1
 
1.9%
254 1
 
1.9%
260 7
13.2%
270 3
5.7%
295 1
 
1.9%
ValueCountFrequency (%)
1140 2
3.8%
1050 1
1.9%
973 2
3.8%
850 1
1.9%
760 1
1.9%
755 2
3.8%
696 1
1.9%
664 2
3.8%
650 1
1.9%
640 1
1.9%

Brakes
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)3.8%
Missing1
Missing (%)1.9%
Memory size552.0 B
disc (front + rear)
45 
disc (front) + drum (rear)

Length

Max length26
Median length19
Mean length19.942308
Min length19

Characters and Unicode

Total characters1037
Distinct characters17
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdisc (front + rear)
2nd rowdisc (front + rear)
3rd rowdisc (front + rear)
4th rowdisc (front + rear)
5th rowdisc (front + rear)

Common Values

ValueCountFrequency (%)
disc (front + rear) 45
84.9%
disc (front) + drum (rear) 7
 
13.2%
(Missing) 1
 
1.9%

Length

2023-10-24T21:59:38.771980image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-24T21:59:38.867725image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
disc 52
24.2%
front 52
24.2%
52
24.2%
rear 52
24.2%
drum 7
 
3.3%

Most occurring characters

ValueCountFrequency (%)
163
15.7%
r 163
15.7%
d 59
 
5.7%
) 59
 
5.7%
( 59
 
5.7%
t 52
 
5.0%
a 52
 
5.0%
e 52
 
5.0%
+ 52
 
5.0%
o 52
 
5.0%
Other values (7) 274
26.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 704
67.9%
Space Separator 163
 
15.7%
Close Punctuation 59
 
5.7%
Open Punctuation 59
 
5.7%
Math Symbol 52
 
5.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 163
23.2%
d 59
 
8.4%
t 52
 
7.4%
a 52
 
7.4%
e 52
 
7.4%
o 52
 
7.4%
n 52
 
7.4%
i 52
 
7.4%
f 52
 
7.4%
c 52
 
7.4%
Other values (3) 66
9.4%
Space Separator
ValueCountFrequency (%)
163
100.0%
Close Punctuation
ValueCountFrequency (%)
) 59
100.0%
Open Punctuation
ValueCountFrequency (%)
( 59
100.0%
Math Symbol
ValueCountFrequency (%)
+ 52
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 704
67.9%
Common 333
32.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 163
23.2%
d 59
 
8.4%
t 52
 
7.4%
a 52
 
7.4%
e 52
 
7.4%
o 52
 
7.4%
n 52
 
7.4%
i 52
 
7.4%
f 52
 
7.4%
c 52
 
7.4%
Other values (3) 66
9.4%
Common
ValueCountFrequency (%)
163
48.9%
) 59
 
17.7%
( 59
 
17.7%
+ 52
 
15.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1037
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
163
15.7%
r 163
15.7%
d 59
 
5.7%
) 59
 
5.7%
( 59
 
5.7%
t 52
 
5.0%
a 52
 
5.0%
e 52
 
5.0%
+ 52
 
5.0%
o 52
 
5.0%
Other values (7) 274
26.4%

Drive
Categorical

Distinct3
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Memory size552.0 B
2WD (front)
24 
4WD
18 
2WD (rear)
11 

Length

Max length11
Median length10
Mean length8.0754717
Min length3

Characters and Unicode

Total characters428
Distinct characters14
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4WD
2nd row4WD
3rd row4WD
4th row4WD
5th row4WD

Common Values

ValueCountFrequency (%)
2WD (front) 24
45.3%
4WD 18
34.0%
2WD (rear) 11
20.8%

Length

2023-10-24T21:59:39.017122image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-24T21:59:39.125192image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
2wd 35
39.8%
front 24
27.3%
4wd 18
20.5%
rear 11
 
12.5%

Most occurring characters

ValueCountFrequency (%)
W 53
12.4%
D 53
12.4%
r 46
10.7%
2 35
8.2%
35
8.2%
( 35
8.2%
) 35
8.2%
f 24
 
5.6%
o 24
 
5.6%
n 24
 
5.6%
Other values (4) 64
15.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 164
38.3%
Uppercase Letter 106
24.8%
Decimal Number 53
 
12.4%
Space Separator 35
 
8.2%
Open Punctuation 35
 
8.2%
Close Punctuation 35
 
8.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 46
28.0%
f 24
14.6%
o 24
14.6%
n 24
14.6%
t 24
14.6%
e 11
 
6.7%
a 11
 
6.7%
Uppercase Letter
ValueCountFrequency (%)
W 53
50.0%
D 53
50.0%
Decimal Number
ValueCountFrequency (%)
2 35
66.0%
4 18
34.0%
Space Separator
ValueCountFrequency (%)
35
100.0%
Open Punctuation
ValueCountFrequency (%)
( 35
100.0%
Close Punctuation
ValueCountFrequency (%)
) 35
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 270
63.1%
Common 158
36.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
W 53
19.6%
D 53
19.6%
r 46
17.0%
f 24
8.9%
o 24
8.9%
n 24
8.9%
t 24
8.9%
e 11
 
4.1%
a 11
 
4.1%
Common
ValueCountFrequency (%)
2 35
22.2%
35
22.2%
( 35
22.2%
) 35
22.2%
4 18
11.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 428
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
W 53
12.4%
D 53
12.4%
r 46
10.7%
2 35
8.2%
35
8.2%
( 35
8.2%
) 35
8.2%
f 24
 
5.6%
o 24
 
5.6%
n 24
 
5.6%
Other values (4) 64
15.0%

Battery
Real number (ℝ)

Distinct24
Distinct (%)45.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.366038
Minimum17.6
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size552.0 B
2023-10-24T21:59:39.245510image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum17.6
5-th percentile30.94
Q140
median58
Q380
95-th percentile100
Maximum100
Range82.4
Interquartile range (IQR)40

Descriptive statistics

Standard deviation24.170913
Coefficient of variation (CV)0.38756532
Kurtosis-1.2048839
Mean62.366038
Median Absolute Deviation (MAD)19
Skewness0.12216038
Sum3305.4
Variance584.23306
MonotonicityNot monotonic
2023-10-24T21:59:39.370922image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
50 7
 
13.2%
95 4
 
7.5%
100 4
 
7.5%
35.5 3
 
5.7%
39.2 3
 
5.7%
64 3
 
5.7%
93.4 3
 
5.7%
71 2
 
3.8%
75 2
 
3.8%
17.6 2
 
3.8%
Other values (14) 20
37.7%
ValueCountFrequency (%)
17.6 2
 
3.8%
28.9 1
 
1.9%
32.3 1
 
1.9%
35.5 3
5.7%
36.8 1
 
1.9%
38.3 1
 
1.9%
39.2 3
5.7%
40 2
 
3.8%
42.2 2
 
3.8%
50 7
13.2%
ValueCountFrequency (%)
100 4
7.5%
95 4
7.5%
93.4 3
5.7%
90 2
3.8%
80 2
3.8%
79.2 1
 
1.9%
77 2
3.8%
75 2
3.8%
71 2
3.8%
64 3
5.7%

Range
Real number (ℝ)

Distinct47
Distinct (%)88.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean376.90566
Minimum148
Maximum652
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size552.0 B
2023-10-24T21:59:39.561724image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum148
5-th percentile200
Q1289
median364
Q3450
95-th percentile572.2
Maximum652
Range504
Interquartile range (IQR)161

Descriptive statistics

Standard deviation118.81794
Coefficient of variation (CV)0.31524583
Kurtosis-0.26508654
Mean376.90566
Median Absolute Deviation (MAD)85
Skewness0.25390095
Sum19976
Variance14117.702
MonotonicityNot monotonic
2023-10-24T21:59:39.736759image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
289 2
 
3.8%
340 2
 
3.8%
395 2
 
3.8%
200 2
 
3.8%
320 2
 
3.8%
222 2
 
3.8%
567 1
 
1.9%
450 1
 
1.9%
412 1
 
1.9%
260 1
 
1.9%
Other values (37) 37
69.8%
ValueCountFrequency (%)
148 1
1.9%
154 1
1.9%
200 2
3.8%
222 2
3.8%
230 1
1.9%
234 1
1.9%
258 1
1.9%
260 1
1.9%
270 1
1.9%
276 1
1.9%
ValueCountFrequency (%)
652 1
1.9%
639 1
1.9%
580 1
1.9%
567 1
1.9%
561 1
1.9%
549 1
1.9%
548 1
1.9%
500 1
1.9%
470 1
1.9%
463 1
1.9%

Wheelbase
Real number (ℝ)

Distinct29
Distinct (%)54.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean273.58113
Minimum187.3
Maximum327.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size552.0 B
2023-10-24T21:59:39.883854image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum187.3
5-th percentile246.52
Q1258.8
median270
Q3290
95-th percentile297.5
Maximum327.5
Range140.2
Interquartile range (IQR)31.2

Descriptive statistics

Standard deviation22.740518
Coefficient of variation (CV)0.083121662
Kurtosis2.8860615
Mean273.58113
Median Absolute Deviation (MAD)16.4
Skewness-0.69901808
Sum14499.8
Variance517.13118
MonotonicityNot monotonic
2023-10-24T21:59:40.001989image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
292.8 6
 
11.3%
270 5
 
9.4%
290 4
 
7.5%
260 4
 
7.5%
287.5 3
 
5.7%
253.8 3
 
5.7%
296.5 2
 
3.8%
257 2
 
3.8%
296 2
 
3.8%
277 2
 
3.8%
Other values (19) 20
37.7%
ValueCountFrequency (%)
187.3 1
 
1.9%
241.7 1
 
1.9%
242.2 1
 
1.9%
249.4 1
 
1.9%
249.5 1
 
1.9%
253.8 3
5.7%
254 1
 
1.9%
255.8 1
 
1.9%
256.1 1
 
1.9%
257 2
3.8%
ValueCountFrequency (%)
327.5 1
 
1.9%
320 1
 
1.9%
299 1
 
1.9%
296.5 2
 
3.8%
296 2
 
3.8%
292.8 6
11.3%
290 4
7.5%
287.5 3
5.7%
287.3 1
 
1.9%
286.4 1
 
1.9%

Length
Real number (ℝ)

Distinct34
Distinct (%)64.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean442.50943
Minimum269.5
Maximum514
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size552.0 B
2023-10-24T21:59:40.133216image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum269.5
5-th percentile359.88
Q1411.8
median447
Q3490.1
95-th percentile500.22
Maximum514
Range244.5
Interquartile range (IQR)78.3

Descriptive statistics

Standard deviation48.86328
Coefficient of variation (CV)0.11042314
Kurtosis1.5455539
Mean442.50943
Median Absolute Deviation (MAD)38.5
Skewness-0.93540243
Sum23453
Variance2387.6201
MonotonicityNot monotonic
2023-10-24T21:59:40.271660image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
490.1 4
 
7.5%
496.3 4
 
7.5%
469 3
 
5.7%
400.6 2
 
3.8%
408.5 2
 
3.8%
490.2 2
 
3.8%
449 2
 
3.8%
497.9 2
 
3.8%
503.7 2
 
3.8%
419.5 2
 
3.8%
Other values (24) 28
52.8%
ValueCountFrequency (%)
269.5 1
1.9%
349.5 1
1.9%
359.7 1
1.9%
360 1
1.9%
384.5 1
1.9%
389.4 2
3.8%
400.6 2
3.8%
405.5 1
1.9%
406 1
1.9%
408.5 2
3.8%
ValueCountFrequency (%)
514 1
 
1.9%
503.7 2
3.8%
497.9 2
3.8%
496.3 4
7.5%
490.2 2
3.8%
490.1 4
7.5%
476.2 1
 
1.9%
473.4 1
 
1.9%
469 3
5.7%
468.2 1
 
1.9%

Width
Real number (ℝ)

Distinct30
Distinct (%)56.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean186.24151
Minimum164.5
Maximum255.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size552.0 B
2023-10-24T21:59:40.405678image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum164.5
5-th percentile166.42
Q1178.8
median180.9
Q3193.5
95-th percentile203.46
Maximum255.8
Range91.3
Interquartile range (IQR)14.7

Descriptive statistics

Standard deviation14.280641
Coefficient of variation (CV)0.076678078
Kurtosis9.7171863
Mean186.24151
Median Absolute Deviation (MAD)8.2
Skewness2.190216
Sum9870.8
Variance203.93671
MonotonicityNot monotonic
2023-10-24T21:59:40.556436image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
180 5
 
9.4%
193.5 4
 
7.5%
196.6 4
 
7.5%
179.1 3
 
5.7%
193 3
 
5.7%
207 2
 
3.8%
197.6 2
 
3.8%
178.8 2
 
3.8%
196.4 2
 
3.8%
180.5 2
 
3.8%
Other values (20) 24
45.3%
ValueCountFrequency (%)
164.5 2
3.8%
166.3 1
1.9%
166.5 1
1.9%
172.7 1
1.9%
174.5 1
1.9%
175.2 2
3.8%
175.5 1
1.9%
176.5 1
1.9%
177 1
1.9%
178.7 2
3.8%
ValueCountFrequency (%)
255.8 1
 
1.9%
207 2
3.8%
201.1 1
 
1.9%
197.6 2
3.8%
196.6 4
7.5%
196.4 2
3.8%
193.5 4
7.5%
193 3
5.7%
192.8 1
 
1.9%
192 1
 
1.9%

Height
Real number (ℝ)

Distinct36
Distinct (%)67.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean155.42264
Minimum137.8
Maximum191
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size552.0 B
2023-10-24T21:59:40.689590image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum137.8
5-th percentile138.02
Q1148.1
median155.6
Q3161.5
95-th percentile174.4
Maximum191
Range53.2
Interquartile range (IQR)13.4

Descriptive statistics

Standard deviation11.275358
Coefficient of variation (CV)0.072546434
Kurtosis2.7714844
Mean155.42264
Median Absolute Deviation (MAD)6
Skewness1.1751044
Sum8237.4
Variance127.13371
MonotonicityNot monotonic
2023-10-24T21:59:40.849959image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
162.9 3
 
5.7%
157 3
 
5.7%
144 3
 
5.7%
144.5 2
 
3.8%
156.2 2
 
3.8%
161.6 2
 
3.8%
153 2
 
3.8%
155.5 2
 
3.8%
160.5 2
 
3.8%
156 2
 
3.8%
Other values (26) 30
56.6%
ValueCountFrequency (%)
137.8 1
 
1.9%
137.9 2
3.8%
138.1 1
 
1.9%
143 1
 
1.9%
143.2 1
 
1.9%
143.3 1
 
1.9%
144 3
5.7%
144.5 2
3.8%
147.5 1
 
1.9%
148.1 1
 
1.9%
ValueCountFrequency (%)
191 1
 
1.9%
190 1
 
1.9%
185.8 1
 
1.9%
166.8 1
 
1.9%
163.1 1
 
1.9%
162.9 3
5.7%
162.6 2
3.8%
162.4 1
 
1.9%
161.6 2
3.8%
161.5 1
 
1.9%

Weight
Real number (ℝ)

Distinct46
Distinct (%)86.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1868.4528
Minimum1035
Maximum2710
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size552.0 B
2023-10-24T21:59:41.002819image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1035
5-th percentile1212.2
Q11530
median1685
Q32370
95-th percentile2635
Maximum2710
Range1675
Interquartile range (IQR)840

Descriptive statistics

Standard deviation470.88087
Coefficient of variation (CV)0.25201646
Kurtosis-1.1761809
Mean1868.4528
Median Absolute Deviation (MAD)245
Skewness0.34690317
Sum99028
Variance221728.79
MonotonicityNot monotonic
2023-10-24T21:59:41.135585image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
1535 3
 
5.7%
1862 2
 
3.8%
2695 2
 
3.8%
2445 2
 
3.8%
1502 2
 
3.8%
1592 2
 
3.8%
2295 1
 
1.9%
2380 1
 
1.9%
2370 1
 
1.9%
1178 1
 
1.9%
Other values (36) 36
67.9%
ValueCountFrequency (%)
1035 1
1.9%
1140 1
1.9%
1178 1
1.9%
1235 1
1.9%
1300 1
1.9%
1440 1
1.9%
1455 1
1.9%
1460 1
1.9%
1502 2
3.8%
1514 1
1.9%
ValueCountFrequency (%)
2710 1
1.9%
2695 2
3.8%
2595 1
1.9%
2565 1
1.9%
2524 1
1.9%
2495 1
1.9%
2464 1
1.9%
2445 2
3.8%
2417 1
1.9%
2391 1
1.9%

Permissible_weight
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct35
Distinct (%)77.8%
Missing8
Missing (%)15.1%
Infinite0
Infinite (%)0.0%
Mean2288.8444
Minimum1310
Maximum3500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size552.0 B
2023-10-24T21:59:41.278930image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1310
5-th percentile1538
Q11916
median2119
Q32870
95-th percentile3130
Maximum3500
Range2190
Interquartile range (IQR)954

Descriptive statistics

Standard deviation557.79603
Coefficient of variation (CV)0.24370203
Kurtosis-1.0207569
Mean2288.8444
Median Absolute Deviation (MAD)389
Skewness0.38710771
Sum102998
Variance311136.41
MonotonicityNot monotonic
2023-10-24T21:59:41.425082image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
3130 4
 
7.5%
2880 3
 
5.7%
1530 2
 
3.8%
1682 2
 
3.8%
1988 2
 
3.8%
3040 2
 
3.8%
1730 2
 
3.8%
2270 1
 
1.9%
1570 1
 
1.9%
1916 1
 
1.9%
Other values (25) 25
47.2%
(Missing) 8
 
15.1%
ValueCountFrequency (%)
1310 1
1.9%
1530 2
3.8%
1570 1
1.9%
1682 2
3.8%
1730 2
3.8%
1770 1
1.9%
1855 1
1.9%
1870 1
1.9%
1916 1
1.9%
1918 1
1.9%
ValueCountFrequency (%)
3500 1
 
1.9%
3130 4
7.5%
3040 2
3.8%
2940 1
 
1.9%
2880 3
5.7%
2870 1
 
1.9%
2810 1
 
1.9%
2725 1
 
1.9%
2670 1
 
1.9%
2660 1
 
1.9%

Capacity
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct35
Distinct (%)77.8%
Missing8
Missing (%)15.1%
Infinite0
Infinite (%)0.0%
Mean520.46667
Minimum290
Maximum1056
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size552.0 B
2023-10-24T21:59:41.527807image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum290
5-th percentile353.4
Q1440
median486
Q3575
95-th percentile726
Maximum1056
Range766
Interquartile range (IQR)135

Descriptive statistics

Standard deviation140.68285
Coefficient of variation (CV)0.27030136
Kurtosis3.9766274
Mean520.46667
Median Absolute Deviation (MAD)61
Skewness1.5630115
Sum23421
Variance19791.664
MonotonicityNot monotonic
2023-10-24T21:59:41.712705image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
485 2
 
3.8%
367 2
 
3.8%
670 2
 
3.8%
445 2
 
3.8%
575 2
 
3.8%
640 2
 
3.8%
450 2
 
3.8%
540 2
 
3.8%
440 2
 
3.8%
565 2
 
3.8%
Other values (25) 25
47.2%
(Missing) 8
 
15.1%
ValueCountFrequency (%)
290 1
1.9%
342 1
1.9%
350 1
1.9%
367 2
3.8%
370 1
1.9%
412 1
1.9%
417 1
1.9%
425 1
1.9%
435 1
1.9%
440 2
3.8%
ValueCountFrequency (%)
1056 1
1.9%
865 1
1.9%
740 1
1.9%
670 2
3.8%
661 1
1.9%
660 1
1.9%
658 1
1.9%
640 2
3.8%
575 2
3.8%
565 2
3.8%

Seats
Real number (ℝ)

Distinct6
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9056604
Minimum2
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size552.0 B
2023-10-24T21:59:41.884036image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q15
median5
Q35
95-th percentile6.4
Maximum8
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.83813325
Coefficient of variation (CV)0.17085024
Kurtosis5.9868289
Mean4.9056604
Median Absolute Deviation (MAD)0
Skewness0.59003848
Sum260
Variance0.70246734
MonotonicityNot monotonic
2023-10-24T21:59:41.987818image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 38
71.7%
4 10
 
18.9%
7 2
 
3.8%
2 1
 
1.9%
8 1
 
1.9%
6 1
 
1.9%
ValueCountFrequency (%)
2 1
 
1.9%
4 10
 
18.9%
5 38
71.7%
6 1
 
1.9%
7 2
 
3.8%
8 1
 
1.9%
ValueCountFrequency (%)
8 1
 
1.9%
7 2
 
3.8%
6 1
 
1.9%
5 38
71.7%
4 10
 
18.9%
2 1
 
1.9%

Doors
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Memory size552.0 B
5.0
47 
4.0
 
4
3.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters159
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row5.0
3rd row5.0
4th row5.0
5th row5.0

Common Values

ValueCountFrequency (%)
5.0 47
88.7%
4.0 4
 
7.5%
3.0 2
 
3.8%

Length

2023-10-24T21:59:42.160847image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-24T21:59:42.454517image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
5.0 47
88.7%
4.0 4
 
7.5%
3.0 2
 
3.8%

Most occurring characters

ValueCountFrequency (%)
. 53
33.3%
0 53
33.3%
5 47
29.6%
4 4
 
2.5%
3 2
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 106
66.7%
Other Punctuation 53
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 53
50.0%
5 47
44.3%
4 4
 
3.8%
3 2
 
1.9%
Other Punctuation
ValueCountFrequency (%)
. 53
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 159
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 53
33.3%
0 53
33.3%
5 47
29.6%
4 4
 
2.5%
3 2
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 159
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 53
33.3%
0 53
33.3%
5 47
29.6%
4 4
 
2.5%
3 2
 
1.3%

Tire_size
Real number (ℝ)

Distinct8
Distinct (%)15.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.679245
Minimum14
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size552.0 B
2023-10-24T21:59:42.590271image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile15
Q116
median17
Q319
95-th percentile20
Maximum21
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.8684999
Coefficient of variation (CV)0.10568889
Kurtosis-1.0562974
Mean17.679245
Median Absolute Deviation (MAD)2
Skewness-0.062969919
Sum937
Variance3.4912917
MonotonicityNot monotonic
2023-10-24T21:59:42.832429image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
19 11
20.8%
16 11
20.8%
17 10
18.9%
20 9
17.0%
18 4
 
7.5%
15 4
 
7.5%
21 2
 
3.8%
14 2
 
3.8%
ValueCountFrequency (%)
14 2
 
3.8%
15 4
 
7.5%
16 11
20.8%
17 10
18.9%
18 4
 
7.5%
19 11
20.8%
20 9
17.0%
21 2
 
3.8%
ValueCountFrequency (%)
21 2
 
3.8%
20 9
17.0%
19 11
20.8%
18 4
 
7.5%
17 10
18.9%
16 11
20.8%
15 4
 
7.5%
14 2
 
3.8%

Max_speed
Real number (ℝ)

Distinct21
Distinct (%)39.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean178.16981
Minimum123
Maximum261
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size552.0 B
2023-10-24T21:59:43.065106image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum123
5-th percentile130
Q1150
median160
Q3200
95-th percentile260.4
Maximum261
Range138
Interquartile range (IQR)50

Descriptive statistics

Standard deviation43.056196
Coefficient of variation (CV)0.2416582
Kurtosis-0.62004925
Mean178.16981
Median Absolute Deviation (MAD)20
Skewness0.84519037
Sum9443
Variance1853.836
MonotonicityNot monotonic
2023-10-24T21:59:43.280280image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
150 7
13.2%
160 6
 
11.3%
130 5
 
9.4%
250 4
 
7.5%
200 3
 
5.7%
261 3
 
5.7%
167 3
 
5.7%
190 2
 
3.8%
260 2
 
3.8%
140 2
 
3.8%
Other values (11) 16
30.2%
ValueCountFrequency (%)
123 1
 
1.9%
130 5
9.4%
135 1
 
1.9%
140 2
 
3.8%
144 1
 
1.9%
145 2
 
3.8%
150 7
13.2%
155 2
 
3.8%
157 2
 
3.8%
160 6
11.3%
ValueCountFrequency (%)
261 3
5.7%
260 2
3.8%
250 4
7.5%
233 1
 
1.9%
225 1
 
1.9%
210 2
3.8%
200 3
5.7%
190 2
3.8%
180 2
3.8%
167 3
5.7%

Boot_capacity
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct31
Distinct (%)59.6%
Missing1
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean445.09615
Minimum171
Maximum870
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size552.0 B
2023-10-24T21:59:43.441681image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum171
5-th percentile199.3
Q1315
median425
Q3558
95-th percentile795.4
Maximum870
Range699
Interquartile range (IQR)243

Descriptive statistics

Standard deviation180.17848
Coefficient of variation (CV)0.40480799
Kurtosis-0.12199542
Mean445.09615
Median Absolute Deviation (MAD)112
Skewness0.70027513
Sum23145
Variance32464.285
MonotonicityNot monotonic
2023-10-24T21:59:43.604927image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
660 3
 
5.7%
260 3
 
5.7%
615 3
 
5.7%
425 3
 
5.7%
315 2
 
3.8%
488 2
 
3.8%
250 2
 
3.8%
435 2
 
3.8%
745 2
 
3.8%
857 2
 
3.8%
Other values (21) 28
52.8%
ValueCountFrequency (%)
171 2
3.8%
185 1
 
1.9%
211 1
 
1.9%
250 2
3.8%
260 3
5.7%
267 1
 
1.9%
310 1
 
1.9%
311 1
 
1.9%
315 2
3.8%
332 2
3.8%
ValueCountFrequency (%)
870 1
 
1.9%
857 2
3.8%
745 2
3.8%
660 3
5.7%
656 1
 
1.9%
615 3
5.7%
603 1
 
1.9%
543 1
 
1.9%
510 1
 
1.9%
500 1
 
1.9%

Acceleration
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct34
Distinct (%)68.0%
Missing3
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean7.36
Minimum2.5
Maximum13.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size552.0 B
2023-10-24T21:59:43.760064image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2.5
5-th percentile2.98
Q14.875
median7.7
Q39.375
95-th percentile12.12
Maximum13.1
Range10.6
Interquartile range (IQR)4.5

Descriptive statistics

Standard deviation2.7866299
Coefficient of variation (CV)0.37861819
Kurtosis-0.69753553
Mean7.36
Median Absolute Deviation (MAD)2
Skewness0.10199939
Sum368
Variance7.7653061
MonotonicityNot monotonic
2023-10-24T21:59:43.927251image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
8.1 3
 
5.7%
6.8 3
 
5.7%
7.9 3
 
5.7%
5.7 2
 
3.8%
9.9 2
 
3.8%
2.8 2
 
3.8%
4 2
 
3.8%
9.7 2
 
3.8%
7.3 2
 
3.8%
9 2
 
3.8%
Other values (24) 27
50.9%
(Missing) 3
 
5.7%
ValueCountFrequency (%)
2.5 1
1.9%
2.8 2
3.8%
3.2 1
1.9%
3.3 1
1.9%
3.8 1
1.9%
4 2
3.8%
4.4 1
1.9%
4.5 2
3.8%
4.6 1
1.9%
4.8 1
1.9%
ValueCountFrequency (%)
13.1 1
1.9%
12.7 1
1.9%
12.3 1
1.9%
11.9 1
1.9%
11.6 1
1.9%
11.4 1
1.9%
9.9 2
3.8%
9.8 1
1.9%
9.7 2
3.8%
9.5 2
3.8%

DC
Real number (ℝ)

Distinct10
Distinct (%)18.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean113.50943
Minimum22
Maximum270
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size552.0 B
2023-10-24T21:59:44.051141image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile38.8
Q1100
median100
Q3150
95-th percentile243
Maximum270
Range248
Interquartile range (IQR)50

Descriptive statistics

Standard deviation57.16697
Coefficient of variation (CV)0.50363188
Kurtosis1.5193799
Mean113.50943
Median Absolute Deviation (MAD)50
Skewness0.9575207
Sum6016
Variance3268.0624
MonotonicityNot monotonic
2023-10-24T21:59:44.184812image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
100 19
35.8%
150 14
26.4%
50 7
 
13.2%
270 3
 
5.7%
110 2
 
3.8%
40 2
 
3.8%
22 2
 
3.8%
125 2
 
3.8%
37 1
 
1.9%
225 1
 
1.9%
ValueCountFrequency (%)
22 2
 
3.8%
37 1
 
1.9%
40 2
 
3.8%
50 7
 
13.2%
100 19
35.8%
110 2
 
3.8%
125 2
 
3.8%
150 14
26.4%
225 1
 
1.9%
270 3
 
5.7%
ValueCountFrequency (%)
270 3
 
5.7%
225 1
 
1.9%
150 14
26.4%
125 2
 
3.8%
110 2
 
3.8%
100 19
35.8%
50 7
 
13.2%
40 2
 
3.8%
37 1
 
1.9%
22 2
 
3.8%

Energy_consumption
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct40
Distinct (%)90.9%
Missing9
Missing (%)17.0%
Infinite0
Infinite (%)0.0%
Mean18.994318
Minimum13.1
Maximum28.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size552.0 B
2023-10-24T21:59:44.312993image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum13.1
5-th percentile14.045
Q115.6
median17.05
Q323.5
95-th percentile27.005
Maximum28.2
Range15.1
Interquartile range (IQR)7.9

Descriptive statistics

Standard deviation4.4182525
Coefficient of variation (CV)0.23260917
Kurtosis-0.9696165
Mean18.994318
Median Absolute Deviation (MAD)1.75
Skewness0.69732052
Sum835.75
Variance19.520955
MonotonicityNot monotonic
2023-10-24T21:59:44.446373image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
16.5 2
 
3.8%
15.9 2
 
3.8%
15.4 2
 
3.8%
15.6 2
 
3.8%
25.1 1
 
1.9%
16.65 1
 
1.9%
17.6 1
 
1.9%
16.4 1
 
1.9%
23.4 1
 
1.9%
24.1 1
 
1.9%
Other values (30) 30
56.6%
(Missing) 9
 
17.0%
ValueCountFrequency (%)
13.1 1
1.9%
13.8 1
1.9%
14 1
1.9%
14.3 1
1.9%
14.5 1
1.9%
15 1
1.9%
15.3 1
1.9%
15.4 2
3.8%
15.45 1
1.9%
15.6 2
3.8%
ValueCountFrequency (%)
28.2 1
1.9%
27.55 1
1.9%
27.2 1
1.9%
25.9 1
1.9%
25.2 1
1.9%
25.1 1
1.9%
24.85 1
1.9%
24.45 1
1.9%
24.1 1
1.9%
23.85 1
1.9%

Interactions

2023-10-24T21:59:35.662392image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:05.732054image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:07.584362image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:09.061354image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:10.492195image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:12.136625image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:13.815103image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:15.859885image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:17.346527image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:19.099803image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:20.730659image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:22.305022image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:23.935735image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:25.521479image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:27.264985image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:29.196840image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:30.852789image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:32.418282image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:33.997661image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:35.751773image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:05.848861image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:07.668923image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:09.135271image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:10.704180image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:12.217347image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:13.920207image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:15.952608image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:17.445928image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:19.184013image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:20.815200image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:22.383508image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:24.030848image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2023-10-24T21:59:09.939106image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:11.635345image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:13.141482image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:14.977875image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:16.809037image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:18.368685image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:20.022048image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:21.720375image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:23.313814image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:24.932189image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:26.470811image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:28.457277image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:30.305876image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:31.868347image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:33.424659image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:35.150732image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:36.715899image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:06.930631image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:08.589871image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:10.026424image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:11.708191image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:13.251652image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:15.353354image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:16.887899image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:18.467773image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:20.092228image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:21.794432image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:23.410573image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:25.022339image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:26.541924image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:28.562455image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:30.386301image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:31.943554image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:33.513297image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:35.219540image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:36.803649image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:07.020667image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:08.679454image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:10.114486image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:11.780343image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:13.335460image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:15.440422image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:16.960346image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:18.556258image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:20.164085image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:21.876484image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:23.505816image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:25.098883image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:26.853276image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:28.686424image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:30.485250image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:32.021944image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:33.593445image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:35.299162image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:36.878962image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:07.125328image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:08.758030image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:10.189912image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:11.859135image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:13.440547image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:15.540944image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:17.031041image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:18.657088image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:20.241340image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:21.992579image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:23.604702image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:25.174864image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:26.947278image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:28.805745image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:30.561848image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:32.105132image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:33.683770image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:35.373916image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:36.954699image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:07.218022image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:08.842807image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:10.263858image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:11.931307image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:13.579859image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:15.632111image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:17.104706image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:18.772188image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:20.326110image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:22.072752image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:23.705899image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:25.253964image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:27.050820image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:28.889903image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:30.638800image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:32.183935image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:33.760247image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:35.447744image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:37.033906image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:07.294939image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:08.914875image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:10.347546image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:11.998364image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:13.648612image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:15.701950image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:17.170821image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:18.924554image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:20.598530image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:22.147308image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:23.784889image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:25.339382image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:27.128355image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:29.001478image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:30.706682image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:32.258733image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:33.841965image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:35.522822image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:37.121534image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:07.503349image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:08.983723image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:10.422135image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:12.061005image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:13.719296image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:15.770744image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:17.252332image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:19.008718image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:20.660618image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:22.222021image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:23.852025image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:25.429038image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:27.192276image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:29.101590image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:30.779041image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:32.336143image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:33.909038image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-24T21:59:35.587265image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2023-10-24T21:59:44.613678image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
PricePowerTorqueBatteryRangeWheelbaseLengthWidthHeightWeightPermissible_weightCapacitySeatsTire_sizeMax_speedBoot_capacityAccelerationDCEnergy_consumptionCARMakeModelBrakesDriveDoors
Price1.0000.9000.8530.8540.6630.8550.8710.8720.1590.8560.8170.7090.1850.8510.8680.743-0.8410.8570.6161.0000.2021.0000.0000.6030.530
Power0.9001.0000.9250.8740.7670.7830.8330.8170.0140.8340.7710.5670.1530.8930.9370.670-0.9600.8530.5501.0000.0541.0000.0000.7040.529
Torque0.8530.9251.0000.8060.6490.7740.8300.8130.0650.8330.7370.5700.2360.8200.8990.689-0.8670.8480.5261.0000.2381.0000.2870.5900.423
Battery0.8540.8740.8061.0000.8520.8310.8530.8250.2000.8750.8470.6290.3240.7760.8380.781-0.8350.8380.6371.0000.5481.0000.5230.7000.410
Range0.6630.7670.6490.8521.0000.6050.6130.6490.0200.6010.5410.3910.2260.6990.8090.536-0.7390.6880.2121.0000.3831.0000.4910.4400.314
Wheelbase0.8550.7830.7740.8310.6051.0000.9410.8450.3180.9140.9250.8330.4730.6970.7250.918-0.7050.7630.6791.0000.5721.0000.7890.7480.474
Length0.8710.8330.8300.8530.6130.9411.0000.8470.1830.9210.9150.8180.3760.7060.7870.902-0.7940.8390.7121.0000.6011.0000.6810.6210.553
Width0.8720.8170.8130.8250.6490.8450.8471.0000.1580.8120.8040.7190.3080.8000.8300.755-0.7290.8250.5011.0000.6091.0000.6040.5850.428
Height0.1590.0140.0650.2000.0200.3180.1830.1581.0000.3530.4170.4140.5050.151-0.0610.3900.113-0.0230.2551.0000.4871.0000.0000.4460.560
Weight0.8560.8340.8330.8750.6010.9140.9210.8120.3531.0000.9620.7140.4400.7350.7380.865-0.7510.8240.7191.0000.4231.0000.7380.5740.501
Permissible_weight0.8170.7710.7370.8470.5410.9250.9150.8040.4170.9621.0000.7520.3780.6560.6860.896-0.7110.8020.7361.0000.4511.0000.5050.6790.624
Capacity0.7090.5670.5700.6290.3910.8330.8180.7190.4140.7140.7521.0000.2600.4960.5790.805-0.5120.6320.5931.0000.3451.0000.0000.5800.331
Seats0.1850.1530.2360.3240.2260.4730.3760.3080.5050.4400.3780.2601.0000.0940.1040.457-0.0570.2150.2081.0000.3841.0000.3480.1140.605
Tire_size0.8510.8930.8200.7760.6990.6970.7060.8000.1510.7350.6560.4960.0941.0000.8610.599-0.8240.7480.3461.0000.3941.0000.5320.5800.153
Max_speed0.8680.9370.8990.8380.8090.7250.7870.830-0.0610.7380.6860.5790.1040.8611.0000.601-0.9230.8790.3981.0000.4111.0000.4840.6320.293
Boot_capacity0.7430.6700.6890.7810.5360.9180.9020.7550.3900.8650.8960.8050.4570.5990.6011.000-0.6510.6570.6611.0000.4361.0000.4190.5450.437
Acceleration-0.841-0.960-0.867-0.835-0.739-0.705-0.794-0.7290.113-0.751-0.711-0.512-0.057-0.824-0.923-0.6511.000-0.815-0.6031.0000.2861.0000.4620.5530.273
DC0.8570.8530.8480.8380.6880.7630.8390.825-0.0230.8240.8020.6320.2150.7480.8790.657-0.8151.0000.6201.0000.5911.0000.7780.5930.686
Energy_consumption0.6160.5500.5260.6370.2120.6790.7120.5010.2550.7190.7360.5930.2080.3460.3980.661-0.6030.6201.0001.0000.3821.0000.0000.5920.335
CAR1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
Make0.2020.0540.2380.5480.3830.5720.6010.6090.4870.4230.4510.3450.3840.3940.4110.4360.2860.5910.3821.0001.0001.0000.8000.7200.724
Model1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
Brakes0.0000.0000.2870.5230.4910.7890.6810.6040.0000.7380.5050.0000.3480.5320.4840.4190.4620.7780.0001.0000.8001.0001.0000.4620.132
Drive0.6030.7040.5900.7000.4400.7480.6210.5850.4460.5740.6790.5800.1140.5800.6320.5450.5530.5930.5921.0000.7201.0000.4621.0000.234
Doors0.5300.5290.4230.4100.3140.4740.5530.4280.5600.5010.6240.3310.6050.1530.2930.4370.2730.6860.3351.0000.7241.0000.1320.2341.000

Missing values

2023-10-24T21:59:37.299952image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-24T21:59:37.577112image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-10-24T21:59:37.743009image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CARMakeModelPricePowerTorqueBrakesDriveBatteryRangeWheelbaseLengthWidthHeightWeightPermissible_weightCapacitySeatsDoorsTire_sizeMax_speedBoot_capacityAccelerationDCEnergy_consumption
0Audi e-tron 55 quattroAudie-tron 55 quattro345700.0360.0664.0disc (front + rear)4WD95.0438.0292.8490.1193.5162.92565.03130.0640.05.05.019.0200.0660.05.7150.024.45
1Audi e-tron 50 quattroAudie-tron 50 quattro308400.0313.0540.0disc (front + rear)4WD71.0340.0292.8490.1193.5162.92445.03040.0670.05.05.019.0190.0660.06.8150.023.80
2Audi e-tron S quattroAudie-tron S quattro414900.0503.0973.0disc (front + rear)4WD95.0364.0292.8490.2197.6162.92695.03130.0565.05.05.020.0210.0660.04.5150.027.55
3Audi e-tron Sportback 50 quattroAudie-tron Sportback 50 quattro319700.0313.0540.0disc (front + rear)4WD71.0346.0292.8490.1193.5161.62445.03040.0640.05.05.019.0190.0615.06.8150.023.30
4Audi e-tron Sportback 55 quattroAudie-tron Sportback 55 quattro357000.0360.0664.0disc (front + rear)4WD95.0447.0292.8490.1193.5161.62595.03130.0670.05.05.019.0200.0615.05.7150.023.85
5Audi e-tron Sportback S quattroAudie-tron Sportback S quattro426200.0503.0973.0disc (front + rear)4WD95.0369.0292.8490.2197.6161.52695.03130.0565.05.05.020.0210.0615.04.5150.027.20
6BMW i3BMWi3169700.0170.0250.0disc (front + rear)2WD (rear)42.2359.0257.0400.6179.1157.01440.01730.0440.04.05.019.0160.0260.08.150.013.10
7BMW i3sBMWi3s184200.0184.0270.0disc (front + rear)2WD (rear)42.2345.0257.0400.6179.1159.01460.01730.0440.04.05.020.0160.0260.06.950.014.30
8BMW iX3BMWiX3282900.0286.0400.0disc (front + rear)2WD (rear)80.0460.0286.4473.4189.1166.82260.02725.0540.05.05.019.0180.0510.06.8150.018.80
9Citroën ë-C4Citroënë-C4125000.0136.0260.0disc (front + rear)2WD (front)50.0350.0266.7435.4180.0152.21541.02000.0459.05.05.016.0150.0380.09.5100.0NaN
CARMakeModelPricePowerTorqueBrakesDriveBatteryRangeWheelbaseLengthWidthHeightWeightPermissible_weightCapacitySeatsDoorsTire_sizeMax_speedBoot_capacityAccelerationDCEnergy_consumption
43Tesla Model S PerformanceTeslaModel S Performance443990.0772.01140.0disc (front + rear)4WD100.0639.0296.0497.9196.4144.52417.0NaNNaN5.05.021.0261.0745.02.5150.0NaN
44Tesla Model X Long Range PlusTeslaModel X Long Range Plus407990.0525.0755.0disc (front + rear)4WD100.0561.0296.5503.7207.0162.62464.0NaNNaN7.05.020.0250.0857.04.6150.0NaN
45Tesla Model X PerformanceTeslaModel X Performance482990.0772.01140.0disc (front + rear)4WD100.0548.0296.5503.7207.0162.62524.0NaNNaN7.05.020.0261.0857.02.8150.0NaN
46Volkswagen e-up!Volkswagene-up!97990.083.0210.0disc (front) + drum (rear)2WD (front)32.3258.0241.7360.0164.5149.21235.01530.0370.04.05.014.0130.0250.011.940.014.0
47Volkswagen ID.3 Pro PerformanceVolkswagenID.3 Pro Performance155890.0204.0310.0disc (front) + drum (rear)2WD (rear)58.0425.0277.0426.1180.9156.81805.02270.0540.05.05.018.0160.0385.07.3100.015.4
48Volkswagen ID.3 Pro SVolkswagenID.3 Pro S179990.0204.0310.0disc (front) + drum (rear)2WD (rear)77.0549.0277.0426.1180.9156.81934.02280.0412.05.05.019.0160.0385.07.9125.015.9
49Volkswagen ID.4 1stVolkswagenID.4 1st202390.0204.0310.0disc (front) + drum (rear)2WD (rear)77.0500.0277.1458.4185.2163.12124.02660.0661.05.05.020.0160.0543.08.5125.018.0
50Citroën ë-Spacetourer (M)Citroënë-Spacetourer (M)215400.0136.0260.0disc (front + rear)2WD (front)50.0230.0327.5459.9192.0190.01969.02810.01056.08.05.016.0130.0603.013.1100.025.2
51Mercedes-Benz EQV (long)Mercedes-BenzEQV (long)339480.0204.0362.0NaN2WD (front)90.0356.0320.0514.0192.8191.02710.03500.0865.06.05.017.0160.0NaNNaN110.028.2
52Nissan e-NV200 evaliaNissane-NV200 evalia164328.0109.0254.0disc (front + rear)2WD (front)40.0200.0272.5456.0175.5185.81592.02250.0658.05.05.015.0123.0870.0NaN50.025.9